mlsanityData to 7 Oct 2026

Augmentation for Learning From Demonstration with Environmental Constraints

arXiv 2210.07015 ↗First posted 13 Oct 2022cs.RO cs.AIcited by 0

On the map: Offline RL and imitation learning from data → Robot learning from demonstration

Abstract (arXiv)

We introduce a Learning from Demonstration (LfD) approach for contact-rich manipulation tasks with articulated mechanisms. The extracted policy from a single human demonstration generalizes to different mechanisms of the same type and is robust against environmental variations. The key to achieving such generalization and robustness from a single human demonstration is to autonomously augment the initial demonstration to gather additional information through purposefully interacting with the environment. Our real-world experiments on complex mechanisms with multi-DOF demonstrate that our approach can reliably accomplish the task in a changing environment. Videos are available at the: https://sites.google.com/view/rbosalfdec/home

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Robot learning from demonstration, one of 13 directions in Offline RL and imitation learning from data. Builds on 14 of its 31 references that are in the map. First posted on arXiv 13 Oct 2022.

Builds on

14 of 31 references.

References · 14

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  2. 2017Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrationscited by 685
  3. 2017Overcoming Exploration in Reinforcement Learning with Demonstrationscited by 322
  4. 2017Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewardscited by 302
  5. 2017Deep Imitation Learning for Complex Manipulation Tasks from Virtual Reality Teleoperationcited by 286
  6. 2017DART: Noise Injection for Robust Imitation Learningcited by 187
  7. 2018Reinforcement and Imitation Learning for Diverse Visuomotor Skillscited by 124
  8. 2017Vision-Based Multi-Task Manipulation for Inexpensive Robots Using End-To-End Learning from Demonstrationcited by 100
  9. 2021Coarse-to-Fine Imitation Learning: Robot Manipulation from a Single Demonstrationcited by 92
  10. 2021A Survey of Robot Manipulation in Contactcited by 26
  11. 2022Surprisingly Robust In-Hand Manipulation: An Empirical Studycited by 16
  12. 2021A Holistic Approach to Reactive Mobile Manipulationcited by 15
2 more
  1. 2022Disturbance-Injected Robust Imitation Learning with Task Achievementcited by 8
  2. 2022"The World Is Its Own Best Model": Robust Real-World Manipulation Through Online Behavior Selectioncited by 1

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  1. 2026From a Single Demonstration to a General Policy for Contact-Rich Manipulation
  2. 2026One-Shot Learning from Demonstration of Contact-Rich Robotic Manipulation by Identifying Physical Interactions
  3. 2018Learning Generalizable Robot Skills from Demonstrations in Cluttered Environments
  4. 2026Learning Forward & Reverse Skills from a Single Unfinished Demonstration for Constrained Manipulation Tasks
  5. 2021Learning Forceful Manipulation Skills from Multi-modal Human Demonstrations
  6. 2026Decompose and Reorganize: Planning with Primitives and Visuomotor Policies Learned from Demonstrations ↗
  7. 2021Learning from Successful and Failed Demonstrations via Optimization
  8. 2023Teaching contact-rich tasks from visual demonstrations by constraint extraction ↗